IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS VOL. 23, NO. 1, JANUARY 2017 501
Manuscript received 31 Mar. 2016; accepted 1 Aug. 2016. Date of publication
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Authoring Data-Driven Videos with DataClips
Fereshteh Amini, Nathalie Henry Riche, Bongshin Lee, Andres Monroy-Hernandez, and Pourang Irani
Abstract—Data videos, or short data-driven motion graphics, are an increasingly popular medium for storytelling. However, creating
data videos is difficult as it involves pulling together a unique combination of skills. We introduce DataClips, an authoring tool aimed
at lowering the barriers to crafting data videos. DataClips allows non-experts to assemble data-driven “clips” together to form longer
sequences. We constructed the library of data clips by analyzing the composition of over 70 data videos produced by reputable
sources such as The New York Times and The Guardian. We demonstrate that DataClips can reproduce over 90% of our data videos
corpus. We also report on a qualitative study comparing the authoring process and outcome achieved by (1) non-experts using
DataClips, and (2) experts using Adobe Illustrator and After Effects to create data-driven clips. Results indicated that non-experts are
able to learn and use DataClips with a short training period. In the span of one hour, they were able to produce more videos than
experts using a professional editing tool, and their clips were rated similarly by an independent audience.
Index Terms—data video, narrative visualization, data storytelling, authoring tools, visualization systems
1 I
NTRODUCTION
The information visualization community has recently focused atten-
tion on empowering data analysts and data enthusiasts in communi-
cating insights through data-driven stories [27][36][44]. A wealth of
data-driven stories can now be found online, as journalists, working
for media outlets such as The New York Times [10] and The Guardian
[7], as well as data enthusiasts [6], craft custom narrative visualiza-
tions for broad audiences [41]. Short data-driven motion graphics, also
known as data videos, which combine both visual and auditory stimuli
to convey a story, have garnered renewed attention [18]. Endowed
with desirable properties, such as having a short duration and engaging
visual effects and animations, data videos are a promising medium for
conveying a data-driven narrative.
Yet, crafting data videos is not an easy task. It requires a significant
amount of time and effort, a broad set of skills, dedicated software or
programming capabilities, and often involves a plethora of tools. For
example, the creation process for a data video [13] can span several
days, involving people with different backgrounds (such as a data an-
alyst generating the data and insights, a scripter crafting the narrative,
along with designers and motion graphics professionals generating the
video material), each of which may hinge on one or more specific soft-
ware tools [28]. The goal of this work is to consolidate the creation of
data videos through the major use of one tool, DataClips, and by low-
ering the skill level required to create data videos using common data
visualizations and animations.
Amini et al. [18] recently explored the components and structure
of a corpus of data videos, identifying different video sequences and
how these sequences play in a narrative (i.e., establisher, initial, peak,
and release). In this work, we take a step further and examine ele-
mental video sequences of data videos composed using animated vis-
ualizations and infographics. We refer to these elemental units, or
building blocks of a data video as data-driven clips. Our examination
of over 70 professionally crafted data videos reveals the presence of
seven major types of data clips across all videos. This led to the devel-
opment of DataClips— a web-based tool allowing data enthusiasts to
compose, edit, and assemble data clips to produce a data video without
Fig. 1. Example of a data-driven video generated by a financial analysis using DataClips. Snapshot images illustrate the content o
f
the video using 9 different clips: animated text, bar chart and line chart depicting the history of sales and salient events, pictograph
and bar chart comparing production and sales, and unit pictograph, bar chart and donut chart illustrating sales during promotion.
Fereshteh Amini and Pourang Irani are with University of Manitoba, Can-
ada. Email: {amini, irani}@cs.umanitoba.ca.
Nathalie Henry Riche, Bongshin Lee, and Andres Monroy-Hernandez are
with Microsoft, E-mail: {nath, bongshin, amh}@microsoft.com.
502 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 23, NO. 1, JANUARY 2017
possessing programming skills. We demonstrate that DataClips covers
a wide range of data videos contained in our corpus, albeit limiting the
level of customization of the visuals and animations. We also report
on a qualitative user study with 12 participants comparing DataClips
to Adobe Illustrator/After Effects software, commonly used to create
data videos. Non-experts in motion graphics could create a larger
number of data videos than those with expertise using the commercial
tool, and with no loss in data video quality.
To summarize, our contributions are threefold: (1) the DataClips
tool; (2) a library of data-driven clips that can be easily extended with
new clips; and (3) a demonstration showing the ability to create data
clips in an efficient manner.
2 R
ELATED WORK
In this section, we provide an overview of prior research around data
storytelling and narrative visualization, as well as existing solutions
for authoring narrative visualizations and data videos.
2.1 Data Storytelling & Narrative Visualization
The visualization literature is primarily focused on methods for visu-
alizing data to facilitate in depth data analysis and exploration. Ideally,
a data analyst can discover new insights from visualizations to com-
municate or convey a story about the data to its stakeholders. Recently,
there has been an upsurge in work transforming data insights into vis-
ual stories [37], through various perspectives [27][36][38]. A number
of visual analytics systems have also integrated storytelling features in
their design (e.g., in-place annotations [25], exporting selected graph-
ical history states [30]). However, supporting an easy creation of rich
and diverse stories based on data insights is still an unsolved problem.
We are witnessing a growing research interest in studying story-
telling techniques for creating more engaging and compelling data sto-
ries or narrative visualizations. In a design space analysis of 58 narra-
tive visualizations, Segel and Heer [41] characterized data videos un-
der different genres. Film and video-based data stories (i.e. data vid-
eos) are recognized among the seven genres of narrative visualization.
Additional work has focused on specific elements of a narrative visu-
alization. Bateman et al. [20] and Borkin et al. [21] specifically fo-
cused on understanding infographics and what makes them appealing
or memorable to a large audience. By studying an online corpus of
narrative visualizations from the area of journalism, Hullman et al.
[32] identified categories of rhetorical techniques affecting reader in-
terpretation. Researchers have also looked at methods of sequencing
[34] and transitioning [19] elements in a narrative visualization.
Data videos have also been studied from the perspective of film
narratives, a medium that bears significant similarity with data videos.
Amini et al. [18] examined 50 data videos and teased apart the various
dimensions of such a storytelling genre with respect to narratives in
film or cinematography. Their results show that data videos exhibit
clear narrative structures and use various presentation styles for ani-
mating and displaying data. While prior work has shed light on the
possibilities of data videos, their structural constituents, and their use
for mass appeal, there has been little research on enabling people to
create such narrative visualizations.
2.2 Authoring Narrative Visualizations
The widespread adoption of infographics in fields such as data jour-
nalism has motivated researchers to investigate ways for making it
easy to author narrative visualizations. One approach involves auto-
matically generating explanatory visualizations from data
[35][33][26]. This is possible by tailoring a specific data visualization
type or dataset. In addition, the storytelling elements of the generated
narrative visualizations are limited to annotations overlaid on data vis-
ualizations, not allowing for a rich data story.
To support diverse data stories and to lower the barriers for creat-
ing narrative visualizations, Satyanarayan and Heer [40] introduced
Ellipsis based on a set of abstractions for storytelling with data visu-
alizations. The graphical user interface of Ellipsis allows people to im-
port data visualizations and add storytelling elements to create multi-
ple scenes. However, such a tool does not consolidate the various fea-
tures necessary to craft a complete data video, based on the concept of
a data clips—a ubiquitous element of major data videos.
2.3 Video-based Storytelling
Video-based storytelling is an active research topic and such media
are also referred to as “annotated videos” or “multimedia presenta-
tions” [39]. Authoring a video-based story involves developing a nar-
rative using a collection of media assets and added annotations.
Bulterman and Hardman [24] identified the key authoring problems to
address when designing an authoring environment for video-based
stories (e.g., the ease of creating unique videos). We argue that similar
paradigm can be extended and considered for authoring data videos.
Shen et al. [42] have developed a video-based authoring system
that suggests candidates for the “next scene” based on semantic rela-
tionships between scenes. Similarly, video story creation tools for
non-experts, such as iMovie Trailers [8] and Animoto [3], provide
templates that help novices follow a fixed narrative structure and ar-
range captured content. However, these tools rely on people to decide
on their own the appropriate types of elements and how to include
them in their stories. To eliminate this burden from authors (especially
since we are targeting non-expert authors), we built DataClips based
on the concept of predefined story abstractions in our library of data
clips, allowing non-experts to rapidly generate a variety of data stories.
3 D
ATACLIPS
3.1 Motivations
The building blocks of data videos are individual data-driven video
sequences, or data clips, each targeting a specific insight of the story
conveyed by an animated visualization. Many data videos found
online are produced by a dedicated department, e.g., The Guardian
visuals [7], or crafted by an independent company [13]. Through an
informal interview [28] with the directors of the company who has
created several data videos [13] as well as data journalists at the recent
Dagstuhl seminar on data-driven storytelling [16], we learned that one
minute of data video, excluding data analysis and insights extraction,
takes about a week’s worth of work from a scripter and an experienced
motion graphic designer. Iterating over the video material is costly as
each sequence involves several hours of work using Adobe Illustrator
[1] for the visual designs and After Effects [2] for the animations.
Thus, a significant time is spent upfront on scripting and storyboard-
ing; but iteration is often unavoidable as clients have trouble envision-
ing the final product without experiencing earlier versions. As design
and animations are customized, updating a video with new data also
requires a significant amount of time. While these comments may not
be representative of the creation of all existing data videos, they give
an idea of the overhead and skills required for their creation. We aim
at lowering the barriers to authoring data videos, to help a wider audi-
ence to use this storytelling medium.
3.2 Usage Scenarios and Target Audience
We closely engaged with two professionals who communicate stories
supported by data on a regular basis: Kate, an investigative journalist,
and Matt, a finance manager. Both are experts in data analysis but have
no expertise with programming or video editing. We met three times
with them, gathering their usage scenarios, relevant data, and creating
data videos included on our companion website [17].
Rapid video prototyping tool. Kate works for a national news outlet.
Her role is to find data and facts, and analyze them to craft news stories
on a variety of topics. Kate finds data videos and animated visualiza-
tions effective for telling a data story to her TV Channel audience on
its website or on social media. She rarely creates them herself, how-
ever, because they require a substantial amount of time and resources
from a dedicated department in her company. She saw the greatest op-
portunities for an authoring tool to support her (1) to quickly craft
short data videos for informal breaking news to be shared on social
media sites, and (2) as a prototyping tool to experiment with different
narratives and ease communication with her graphics department
when producing a high-end data video.
Data video clips authoring tool. Matt’s role is to report on financial
results and opportunities for a series of products. Matt spends about a
fourth of his time compiling presentations to report to executives in
the company. Matt found short animated visualizations (illustrating a
single insight) the most compelling to “bring dry and static charts to
life” in presentations and reports. He mentioned that dues to time con-
straints, he does not create such animations in Microsoft PowerPoint
or other tools, especially as they are tedious to update (for each quarter
and each product). Figure 1 shows clips created with our tool based on
Matt’s data and insights to support a story of sales and the evolution
of promotional events affecting the sales.
3.3 Design Considerations
Considering motivations and scenarios, we settled on four design con-
siderations (DCs) for an authoring tool for non-programmers and non-
video producers. Our premise is that the author has already collected
and analyzed data to extract a set of insights for the video.
(DC1) Lower the barrier for authoring data videos. We strive to
strike a balance between predefined templates and customizable data-
driven videos. Our target audience includes those who have collected
and explored their data but are unlikely to have the skills or time to
master visual design skills or video editing software. While video tem-
plates (e.g., iMovie Trailers) are easiest to create, they are unlikely to
cover the wide range of stories people can tell with their data [18]. We
propose to rely on a set of templates for short video clips that authors
can populate with their data and sequence together.
(DC2) Emphasize pictographs. The use of pictographs or isotypes is
heavily present in data videos [8]. Such icon-based data visualizations,
reinforces data semantics, may require less interpretation time and in-
crease story retention [20][29]. However, most editing tools [1] today
only support manual graphical creation, leading to inaccurate visual
encodings. Our goal is to support the creation of accurate animated
pictographs by generating them from data.
(DC3) Support data-driven attention cues. Attention cues and strat-
egies are extensively used in data videos to engage viewers and guide
their attention during the delivery of a story [18]. For example, it is
common to progressively disclose annotations while highlighting re-
lated elements within a data visualization. We aim at supporting the
creation of data-driven attention cues, enabling authors to import them
along with the data, rather than adding them manually on a case-by-
case basis. We also propose to include animated transitions between
visualizations [31]. Such animated transitions are uncommon in data
videos today as they are complicated to craft.
(DC4) One-of-a-kind data video. Our first goal is to provide an au-
thoring tool for novices with a reasonable level of customization: to
easily create videos with a different look and feel. For example, rather
than enabling users to select the animation timing and behavior of each
individual element of a visualization (as PowerPoint does), we chose
to enable users to have controlled timing for sets of elements (e.g. axes
and bars in a bar chart). Our architecture is modular: it allows ad-
vanced users, able to produce code, to easily extend the capabilities of
the tool by adding clips.
3.4 User Interface
With the considerations above in mind, we implemented DataClips
(http://hci.cs.umanitoba.ca/projects-and-research/details/dataclips).
Its interface (Figure 2) is composed of three panels:
1. Clip Library, populated with a set of data-driven clips we describe
in detail in section 4;
2. My Clips, a workspace panel where clips are previewed and se-
quenced to form a longer video, and;
3. Clip Configuration panel, where users can assign data to each indi-
vidual clip and customize its visual appearance.
We illustrate the main components and features of DataClips through
the creation of a short video. Let us imagine Emma, InfoVis paper co-
chair this year, who would like to create a short data-driven video to
illustrate statistics on the conference attendance and its evolution over
the past five years. Emma has gathered the data into a spreadsheet and
collected a number of insights to communicate the evolution of the
number and gender of authors over the years.
Fig. 2. Annotated screenshot of DataClips tool interface: a) saved clip sequences, b) clip preview and sequencing panel, c) the clip library panel,
d) clip configuration panel, e) import new data, f) clear all clips in preview/sequencing panel, g) category of clips for filling pictographs, h) data
configuration options and corresponding input boxes, and i) helper images including numbered items corresponding to the input boxes, j) visual
and animation configuration options and corresponding input fields.
AMINI ET AL.: AUTHORING DATA-DRIVEN VIDEOS WITH DATACLIPS 503
possessing programming skills. We demonstrate that DataClips covers
a wide range of data videos contained in our corpus, albeit limiting the
level of customization of the visuals and animations. We also report
on a qualitative user study with 12 participants comparing DataClips
to Adobe Illustrator/After Effects software, commonly used to create
data videos. Non-experts in motion graphics could create a larger
number of data videos than those with expertise using the commercial
tool, and with no loss in data video quality.
To summarize, our contributions are threefold: (1) the DataClips
tool; (2) a library of data-driven clips that can be easily extended with
new clips; and (3) a demonstration showing the ability to create data
clips in an efficient manner.
2 R
ELATED WORK
In this section, we provide an overview of prior research around data
storytelling and narrative visualization, as well as existing solutions
for authoring narrative visualizations and data videos.
2.1 Data Storytelling & Narrative Visualization
The visualization literature is primarily focused on methods for visu-
alizing data to facilitate in depth data analysis and exploration. Ideally,
a data analyst can discover new insights from visualizations to com-
municate or convey a story about the data to its stakeholders. Recently,
there has been an upsurge in work transforming data insights into vis-
ual stories [37], through various perspectives [27][36][38]. A number
of visual analytics systems have also integrated storytelling features in
their design (e.g., in-place annotations [25], exporting selected graph-
ical history states [30]). However, supporting an easy creation of rich
and diverse stories based on data insights is still an unsolved problem.
We are witnessing a growing research interest in studying story-
telling techniques for creating more engaging and compelling data sto-
ries or narrative visualizations. In a design space analysis of 58 narra-
tive visualizations, Segel and Heer [41] characterized data videos un-
der different genres. Film and video-based data stories (i.e. data vid-
eos) are recognized among the seven genres of narrative visualization.
Additional work has focused on specific elements of a narrative visu-
alization. Bateman et al. [20] and Borkin et al. [21] specifically fo-
cused on understanding infographics and what makes them appealing
or memorable to a large audience. By studying an online corpus of
narrative visualizations from the area of journalism, Hullman et al.
[32] identified categories of rhetorical techniques affecting reader in-
terpretation. Researchers have also looked at methods of sequencing
[34] and transitioning [19] elements in a narrative visualization.
Data videos have also been studied from the perspective of film
narratives, a medium that bears significant similarity with data videos.
Amini et al. [18] examined 50 data videos and teased apart the various
dimensions of such a storytelling genre with respect to narratives in
film or cinematography. Their results show that data videos exhibit
clear narrative structures and use various presentation styles for ani-
mating and displaying data. While prior work has shed light on the
possibilities of data videos, their structural constituents, and their use
for mass appeal, there has been little research on enabling people to
create such narrative visualizations.
2.2 Authoring Narrative Visualizations
The widespread adoption of infographics in fields such as data jour-
nalism has motivated researchers to investigate ways for making it
easy to author narrative visualizations. One approach involves auto-
matically generating explanatory visualizations from data
[35][33][26]. This is possible by tailoring a specific data visualization
type or dataset. In addition, the storytelling elements of the generated
narrative visualizations are limited to annotations overlaid on data vis-
ualizations, not allowing for a rich data story.
To support diverse data stories and to lower the barriers for creat-
ing narrative visualizations, Satyanarayan and Heer [40] introduced
Ellipsis based on a set of abstractions for storytelling with data visu-
alizations. The graphical user interface of Ellipsis allows people to im-
port data visualizations and add storytelling elements to create multi-
ple scenes. However, such a tool does not consolidate the various fea-
tures necessary to craft a complete data video, based on the concept of
a data clips—a ubiquitous element of major data videos.
2.3 Video-based Storytelling
Video-based storytelling is an active research topic and such media
are also referred to as “annotated videos” or “multimedia presenta-
tions” [39]. Authoring a video-based story involves developing a nar-
rative using a collection of media assets and added annotations.
Bulterman and Hardman [24] identified the key authoring problems to
address when designing an authoring environment for video-based
stories (e.g., the ease of creating unique videos). We argue that similar
paradigm can be extended and considered for authoring data videos.
Shen et al. [42] have developed a video-based authoring system
that suggests candidates for the “next scene” based on semantic rela-
tionships between scenes. Similarly, video story creation tools for
non-experts, such as iMovie Trailers [8] and Animoto [3], provide
templates that help novices follow a fixed narrative structure and ar-
range captured content. However, these tools rely on people to decide
on their own the appropriate types of elements and how to include
them in their stories. To eliminate this burden from authors (especially
since we are targeting non-expert authors), we built DataClips based
on the concept of predefined story abstractions in our library of data
clips, allowing non-experts to rapidly generate a variety of data stories.
3 D
ATACLIPS
3.1 Motivations
The building blocks of data videos are individual data-driven video
sequences, or data clips, each targeting a specific insight of the story
conveyed by an animated visualization. Many data videos found
online are produced by a dedicated department, e.g., The Guardian
visuals [7], or crafted by an independent company [13]. Through an
informal interview [28] with the directors of the company who has
created several data videos [13] as well as data journalists at the recent
Dagstuhl seminar on data-driven storytelling [16], we learned that one
minute of data video, excluding data analysis and insights extraction,
takes about a week’s worth of work from a scripter and an experienced
motion graphic designer. Iterating over the video material is costly as
each sequence involves several hours of work using Adobe Illustrator
[1] for the visual designs and After Effects [2] for the animations.
Thus, a significant time is spent upfront on scripting and storyboard-
ing; but iteration is often unavoidable as clients have trouble envision-
ing the final product without experiencing earlier versions. As design
and animations are customized, updating a video with new data also
requires a significant amount of time. While these comments may not
be representative of the creation of all existing data videos, they give
an idea of the overhead and skills required for their creation. We aim
at lowering the barriers to authoring data videos, to help a wider audi-
ence to use this storytelling medium.
3.2 Usage Scenarios and Target Audience
We closely engaged with two professionals who communicate stories
supported by data on a regular basis: Kate, an investigative journalist,
and Matt, a finance manager. Both are experts in data analysis but have
no expertise with programming or video editing. We met three times
with them, gathering their usage scenarios, relevant data, and creating
data videos included on our companion website [17].
Rapid video prototyping tool. Kate works for a national news outlet.
Her role is to find data and facts, and analyze them to craft news stories
on a variety of topics. Kate finds data videos and animated visualiza-
tions effective for telling a data story to her TV Channel audience on
its website or on social media. She rarely creates them herself, how-
ever, because they require a substantial amount of time and resources
from a dedicated department in her company. She saw the greatest op-
portunities for an authoring tool to support her (1) to quickly craft
short data videos for informal breaking news to be shared on social
media sites, and (2) as a prototyping tool to experiment with different
narratives and ease communication with her graphics department
when producing a high-end data video.
Data video clips authoring tool. Matt’s role is to report on financial
results and opportunities for a series of products. Matt spends about a
fourth of his time compiling presentations to report to executives in
the company. Matt found short animated visualizations (illustrating a
single insight) the most compelling to “bring dry and static charts to
life” in presentations and reports. He mentioned that dues to time con-
straints, he does not create such animations in Microsoft PowerPoint
or other tools, especially as they are tedious to update (for each quarter
and each product). Figure 1 shows clips created with our tool based on
Matt’s data and insights to support a story of sales and the evolution
of promotional events affecting the sales.
3.3 Design Considerations
Considering motivations and scenarios, we settled on four design con-
siderations (DCs) for an authoring tool for non-programmers and non-
video producers. Our premise is that the author has already collected
and analyzed data to extract a set of insights for the video.
(DC1) Lower the barrier for authoring data videos. We strive to
strike a balance between predefined templates and customizable data-
driven videos. Our target audience includes those who have collected
and explored their data but are unlikely to have the skills or time to
master visual design skills or video editing software. While video tem-
plates (e.g., iMovie Trailers) are easiest to create, they are unlikely to
cover the wide range of stories people can tell with their data [18]. We
propose to rely on a set of templates for short video clips that authors
can populate with their data and sequence together.
(DC2) Emphasize pictographs. The use of pictographs or isotypes is
heavily present in data videos [8]. Such icon-based data visualizations,
reinforces data semantics, may require less interpretation time and in-
crease story retention [20][29]. However, most editing tools [1] today
only support manual graphical creation, leading to inaccurate visual
encodings. Our goal is to support the creation of accurate animated
pictographs by generating them from data.
(DC3) Support data-driven attention cues. Attention cues and strat-
egies are extensively used in data videos to engage viewers and guide
their attention during the delivery of a story [18]. For example, it is
common to progressively disclose annotations while highlighting re-
lated elements within a data visualization. We aim at supporting the
creation of data-driven attention cues, enabling authors to import them
along with the data, rather than adding them manually on a case-by-
case basis. We also propose to include animated transitions between
visualizations [31]. Such animated transitions are uncommon in data
videos today as they are complicated to craft.
(DC4) One-of-a-kind data video. Our first goal is to provide an au-
thoring tool for novices with a reasonable level of customization: to
easily create videos with a different look and feel. For example, rather
than enabling users to select the animation timing and behavior of each
individual element of a visualization (as PowerPoint does), we chose
to enable users to have controlled timing for sets of elements (e.g. axes
and bars in a bar chart). Our architecture is modular: it allows ad-
vanced users, able to produce code, to easily extend the capabilities of
the tool by adding clips.
3.4 User Interface
With the considerations above in mind, we implemented DataClips
(http://hci.cs.umanitoba.ca/projects-and-research/details/dataclips).
Its interface (Figure 2) is composed of three panels:
1. Clip Library, populated with a set of data-driven clips we describe
in detail in section 4;
2. My Clips, a workspace panel where clips are previewed and se-
quenced to form a longer video, and;
3. Clip Configuration panel, where users can assign data to each indi-
vidual clip and customize its visual appearance.
We illustrate the main components and features of DataClips through
the creation of a short video. Let us imagine Emma, InfoVis paper co-
chair this year, who would like to create a short data-driven video to
illustrate statistics on the conference attendance and its evolution over
the past five years. Emma has gathered the data into a spreadsheet and
collected a number of insights to communicate the evolution of the
number and gender of authors over the years.
Fig. 2. Annotated screenshot of DataClips tool interface: a) saved clip sequences, b) clip preview and sequencing panel, c) the clip library panel,
d) clip configuration panel, e) import new data, f) clear all clips in preview/sequencing panel, g) category of clips for filling pictographs, h) data
configuration options and corresponding input boxes, and i) helper images including numbered items corresponding to the input boxes, j) visual
and animation configuration options and corresponding input fields.
504 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 23, NO. 1, JANUARY 2017
3.4.1 Familiarization and Data Import
Emma connects to DataClips on the web and takes a first look at an
example story including five clips already loaded into the tool (Figure
2a). She goes through the story sequence in the workspace (Figure 2b)
and selects each clip in turn, which triggers the selected clip’s config-
uration options to appear below the workspace in the configuration
panel (Figure 2d). Then, she explores the Clip library panel by placing
her mouse pointer on the images in Figure 2c to play the animations.
Emma decides to import her data (Figure 2e). DataClips currently
supports a specific data format and does not handle data manipulation
within the tool. Thus, she copies and pastes her data table from a
spreadsheet software (Table 1) into the data import form in DataClip.
The example sequence in her workspace is populated with the new
data and she can now review each clip loaded with her data values.
Clips requiring specific data columns such as ones figuring maps ap-
pear unavailable as her table does not contain geographical data.
Emma clears her workspace (Figure 2f) and starts from scratch.
Year
Number of
Authors
Number
of Males
Number of
Females
Notes on Trends
2012 471 306 165
30% Increase in
Authorship
2013 303 158 145 US Recession
2014 510 301 209 InfoVis in Paris
Table 1. Fictitious sample dataset for InfoVis attendees.
3.4.2 Data Configuration
Emma’s attention moves to the filled stick figure among the picto-
graph-based clips category (Figure 2g). She wants to see what it would
look like with the percentage of female attendees. She drags the clip
to the workspace, and upon dropping it into the My Clip panel, the
animation plays with an automatic assignment of her data columns.
However, the DataClips default assignment does not show the number
of females, but the number of males instead. She selects the clip, which
brings up its configuration options in the panel below.
Emma notices that the data binding (Figure 2h) is composed of two
columns: (1) a column with all her data attribute names separated
based on post-processing of the data types (i.e., dimensions or catego-
ries vs. measures or values) and (2) a column containing three input
boxes which are populated by the system based on the type of im-
ported data columns. To ease the data configuration, a helper image of
the visualization conveys the binding between data column and visual
encoding (Figure 2i). Emma replaces the column “Number of Males”
by the column “Number of Females” in the first box to populate the
clip with her target data column and continues by adding an additional
clip: a line chart with arrow annotations. Each clip has a different set
of input boxes depending on its data requirement. Overall, there are
six types of input boxes to bind to data, the ones noted with a * are
required for each clip, others are optional depending on the clip:
1. Categories*: column names of categorical data attributes such as
date-time or geolocation. Emma places her column “year” in this box
for creating her line chart.
2. Selected Categories: a specific value of the attribute selected in
above (for filtering purposes). For example, dragging and dropping a
subset of years allows her to use only this subset for her line chart
instead of all available ones.
3. Values*: column names of numerical data attributes. For example,
Emma selects “Number of Authors” for populating the y-axis of the
line chart clip she has added to her clip sequence panel.
4. Base Values: are used by clips depicting ratio of values out of a total
(e.g., percentage). Emma populated the filled icon with “Number of
Females” as values and “Number of Authors” as base values.
5. Drill-down/Roll-up Values: animations involving drill-down and
roll-up operations require an additional data attribute to be specified.
For example, after creating her line chart, Emma could select and add
a “line to pictograph” clip that will drill-down into a specific year and
show the percentage of females. She would then drag the year value
into this column.
6. Annotations: column name containing textual annotations associ-
ated with specific values. For the line chart Emma is creating, she
places the column name “Notes on Trends” in this box. As the line is
drawn, the animation pauses and displays the annotation if present.
3.4.3 Visual Configuration
Each clip also has a set of options for the configuration of its visuals
(Figure 2j). The visual configuration is composed of two columns: a
column with options to select from, and a column with fields and
widgets to adjust parameters. Overall, there are five options depending
on the clip type:
1. Color and background: allows selection of color palettes and back-
ground image for each clip. The selection populates the next clips
dropped in the workspace.
2. Icon: allows selection of different icons for pictographs.
3. Axes and orientation: allows displaying axes (present or not) and
changing orientation (vertical or horizontal)
4. Title and legend: modifies the visibility, content, and positioning of
title and legends displaying attributes’ values.
5. Animation style and timing: style refers to the ordering and staging
of the animations for the visual elements, which vary for each clip. For
example, bars in a bar chart can appear “staggered” or “together.” The
user also sets the timing of the entire clip.
3.4.4 Clip Sequencing and Export
Emma has now created two clips. However, she would like to first
play the line chart showing the evolution in number of authors, and
then display the number of females as a filled pictograph for the cur-
rent year. To rearrange the order of the clips, Emma simply drags the
line chart into the first position. Emma then saves her sequence (stored
locally in her browser for later edits) and exports the current version
as a video file that she saves to her disk (Figure 2e).
3.5 Iterative Design
To investigate the usability of the interface, we performed an hour-
long usability session with five users from diverse backgrounds: two
storytelling experts with no expertise in video editing and program-
ming, one graphic designer, and two motion graphics editing experts.
We asked our participants to reproduce an existing video [14] from a
printed storyboard, and observed usability issues as they executed the
task with DataClips. We iterated over the interface design as follows:
Interface layout and icons. We rearranged the position and visibility
of the three major panels and their content to better match the observed
authoring workflow: selection → sequencing → configuration
. We
initially used static icons representing each clip within the library
panel and organized them by type of clip as described in section 4.2.
They were organized by role in the narrative rather than by visualiza-
tion types, and used static icons to convey the fact that they can apply
to any data. However, participants spent a long time finding the clip
they had in mind, and icons failed to depict the actual animations.
Thus, we designed animated icons that would show the clip format as
the user hovers the cursor over the icon.
Assignment of data attributes. Perhaps the most salient change in
the interface was the data configuration. Participants were unable to
understand the terminology and assign data attributes to input boxes
configuring the clip. We iterated over several terms, but none was sat-
isfactory. To solve this, we created helper images including numbered
items corresponding to the input boxes (Figure 2i). We also pre-pop-
ulated each box based on the column types.
4 A
L
IBRARY OF
D
ATA
-D
RIVEN
C
LIPS
In this section, we describe our methodology for selecting the visuali-
zations and clips to create DataClips’ library.
4.1 Methodology
To compose a library of clips that would enable the creation of a broad
range of data videos, we examined a corpus of over 70 data videos
available on news media, government and research center websites,
visualization blogs, and online video portals such as YouTube.com
and Vimeo.com. We used several search keywords such as “data
video,” “animated infographic,” “infographic video,” “motion info-
graphic,“ etc. and processed the top returned results. We kept videos
that (1) presented arguments supported by data, and (2) included at
least one data visualization.
We proceeded to segment each video from this corpus into clips.
We grouped the clips into different categories based on the clip’s role
in the narrative (e.g., introducing by setting up the scene, explaining a
fact in data using annotation, etc.). We then counted the occurrences
of each type of clip, excluding the same animated visualization of the
same data. For example, the data video in [12] includes an animated
bar chart with growing bars three different times throughout the video,
but it reuses the same dataset; hence, we counted it a single time.
Our analysis led to seven types of clips that we describe below,
each applied to eight types of visualizations most commonly found in
the videos. Figure 3 gives an overview of the visualization type by
type of clip. Note that each clip × visualization combination has dif-
ferent variations. For example, a line chart can be created by drawing
both axes and lines together, or one after the other. Figure 4 shows the
frequency of clip types and visualization types in our corpus.
Fig. 4. Frequency of types of clips and data visualizations.
Fig. 3. Taxonomy of clip types as a function of visualization types (rows) and animation types (columns). Icons and descriptions inside cells
show one example implementation for each type.
AMINI ET AL.: AUTHORING DATA-DRIVEN VIDEOS WITH DATACLIPS 505
3.4.1 Familiarization and Data Import
Emma connects to DataClips on the web and takes a first look at an
example story including five clips already loaded into the tool (Figure
2a). She goes through the story sequence in the workspace (Figure 2b)
and selects each clip in turn, which triggers the selected clip’s config-
uration options to appear below the workspace in the configuration
panel (Figure 2d). Then, she explores the Clip library panel by placing
her mouse pointer on the images in Figure 2c to play the animations.
Emma decides to import her data (Figure 2e). DataClips currently
supports a specific data format and does not handle data manipulation
within the tool. Thus, she copies and pastes her data table from a
spreadsheet software (Table 1) into the data import form in DataClip.
The example sequence in her workspace is populated with the new
data and she can now review each clip loaded with her data values.
Clips requiring specific data columns such as ones figuring maps ap-
pear unavailable as her table does not contain geographical data.
Emma clears her workspace (Figure 2f) and starts from scratch.
Year
Number of
Authors
Number
of Males
Number of
Females
Notes on Trends
2012 471 306 165
30% Increase in
Authorship
2013 303 158 145 US Recession
2014 510 301 209 InfoVis in Paris
Table 1. Fictitious sample dataset for InfoVis attendees.
3.4.2 Data Configuration
Emma’s attention moves to the filled stick figure among the picto-
graph-based clips category (Figure 2g). She wants to see what it would
look like with the percentage of female attendees. She drags the clip
to the workspace, and upon dropping it into the My Clip panel, the
animation plays with an automatic assignment of her data columns.
However, the DataClips default assignment does not show the number
of females, but the number of males instead. She selects the clip, which
brings up its configuration options in the panel below.
Emma notices that the data binding (Figure 2h) is composed of two
columns: (1) a column with all her data attribute names separated
based on post-processing of the data types (i.e., dimensions or catego-
ries vs. measures or values) and (2) a column containing three input
boxes which are populated by the system based on the type of im-
ported data columns. To ease the data configuration, a helper image of
the visualization conveys the binding between data column and visual
encoding (Figure 2i). Emma replaces the column “Number of Males”
by the column “Number of Females” in the first box to populate the
clip with her target data column and continues by adding an additional
clip: a line chart with arrow annotations. Each clip has a different set
of input boxes depending on its data requirement. Overall, there are
six types of input boxes to bind to data, the ones noted with a * are
required for each clip, others are optional depending on the clip:
1. Categories*: column names of categorical data attributes such as
date-time or geolocation. Emma places her column “year” in this box
for creating her line chart.
2. Selected Categories: a specific value of the attribute selected in
above (for filtering purposes). For example, dragging and dropping a
subset of years allows her to use only this subset for her line chart
instead of all available ones.
3. Values*: column names of numerical data attributes. For example,
Emma selects “Number of Authors” for populating the y-axis of the
line chart clip she has added to her clip sequence panel.
4. Base Values: are used by clips depicting ratio of values out of a total
(e.g., percentage). Emma populated the filled icon with “Number of
Females” as values and “Number of Authors” as base values.
5. Drill-down/Roll-up Values: animations involving drill-down and
roll-up operations require an additional data attribute to be specified.
For example, after creating her line chart, Emma could select and add
a “line to pictograph” clip that will drill-down into a specific year and
show the percentage of females. She would then drag the year value
into this column.
6. Annotations: column name containing textual annotations associ-
ated with specific values. For the line chart Emma is creating, she
places the column name “Notes on Trends” in this box. As the line is
drawn, the animation pauses and displays the annotation if present.
3.4.3 Visual Configuration
Each clip also has a set of options for the configuration of its visuals
(Figure 2j). The visual configuration is composed of two columns: a
column with options to select from, and a column with fields and
widgets to adjust parameters. Overall, there are five options depending
on the clip type:
1. Color and background: allows selection of color palettes and back-
ground image for each clip. The selection populates the next clips
dropped in the workspace.
2. Icon: allows selection of different icons for pictographs.
3. Axes and orientation: allows displaying axes (present or not) and
changing orientation (vertical or horizontal)
4. Title and legend: modifies the visibility, content, and positioning of
title and legends displaying attributes’ values.
5. Animation style and timing: style refers to the ordering and staging
of the animations for the visual elements, which vary for each clip. For
example, bars in a bar chart can appear “staggered” or “together.” The
user also sets the timing of the entire clip.
3.4.4 Clip Sequencing and Export
Emma has now created two clips. However, she would like to first
play the line chart showing the evolution in number of authors, and
then display the number of females as a filled pictograph for the cur-
rent year. To rearrange the order of the clips, Emma simply drags the
line chart into the first position. Emma then saves her sequence (stored
locally in her browser for later edits) and exports the current version
as a video file that she saves to her disk (Figure 2e).
3.5 Iterative Design
To investigate the usability of the interface, we performed an hour-
long usability session with five users from diverse backgrounds: two
storytelling experts with no expertise in video editing and program-
ming, one graphic designer, and two motion graphics editing experts.
We asked our participants to reproduce an existing video [14] from a
printed storyboard, and observed usability issues as they executed the
task with DataClips. We iterated over the interface design as follows:
Interface layout and icons. We rearranged the position and visibility
of the three major panels and their content to better match the observed
authoring workflow: selection → sequencing → configuration
. We
initially used static icons representing each clip within the library
panel and organized them by type of clip as described in section 4.2.
They were organized by role in the narrative rather than by visualiza-
tion types, and used static icons to convey the fact that they can apply
to any data. However, participants spent a long time finding the clip
they had in mind, and icons failed to depict the actual animations.
Thus, we designed animated icons that would show the clip format as
the user hovers the cursor over the icon.
Assignment of data attributes. Perhaps the most salient change in
the interface was the data configuration. Participants were unable to
understand the terminology and assign data attributes to input boxes
configuring the clip. We iterated over several terms, but none was sat-
isfactory. To solve this, we created helper images including numbered
items corresponding to the input boxes (Figure 2i). We also pre-pop-
ulated each box based on the column types.
4 A
L
IBRARY OF
D
ATA
-D
RIVEN
C
LIPS
In this section, we describe our methodology for selecting the visuali-
zations and clips to create DataClips’ library.
4.1 Methodology
To compose a library of clips that would enable the creation of a broad
range of data videos, we examined a corpus of over 70 data videos
available on news media, government and research center websites,
visualization blogs, and online video portals such as YouTube.com
and Vimeo.com. We used several search keywords such as “data
video,” “animated infographic,” “infographic video,” “motion info-
graphic,“ etc. and processed the top returned results. We kept videos
that (1) presented arguments supported by data, and (2) included at
least one data visualization.
We proceeded to segment each video from this corpus into clips.
We grouped the clips into different categories based on the clip’s role
in the narrative (e.g., introducing by setting up the scene, explaining a
fact in data using annotation, etc.). We then counted the occurrences
of each type of clip, excluding the same animated visualization of the
same data. For example, the data video in [12] includes an animated
bar chart with growing bars three different times throughout the video,
but it reuses the same dataset; hence, we counted it a single time.
Our analysis led to seven types of clips that we describe below,
each applied to eight types of visualizations most commonly found in
the videos. Figure 3 gives an overview of the visualization type by
type of clip. Note that each clip × visualization combination has dif-
ferent variations. For example, a line chart can be created by drawing
both axes and lines together, or one after the other. Figure 4 shows the
frequency of clip types and visualization types in our corpus.
Fig. 4. Frequency of types of clips and data visualizations.
Fig. 3. Taxonomy of clip types as a function of visualization types (rows) and animation types (columns). Icons and descriptions inside cells
show one example implementation for each type.
506 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 23, NO. 1, JANUARY 2017
4.2 Visualization Types
DataClips supports eight types of visualization: four standard charts,
and four pictographs representations (DC2), most commonly found in
data videos.
Standard charts (maps, bar, line, and donut charts). Figure 5a–d
shows standard charts in DataClips and the first column in Figure 5
shows the relative percentages found in our corpus. Note that, in line
with [8], more than half (59%) of the total data visualizations we ob-
served are standard charts.
Pictograph representations. Figure 5e–h present pictographs or iso-
types (International System of TYpographic Picture Education), en-
coding data using pictorial representations (icons). In the simplest
form, a pictograph or pictorial unit bar graph [23] divides the value to
encode into equal portions, each represented by one icon (Figure 5e).
Different categories can be distinguished by changing the icon shape
or color (Figure 5f). A variation of pictographs uses colored icons
(Figure 5g) to encode ratios and percentages. The proportion is repre-
sented by coloring n icons out of a total of m representing the total
value. Note that these representations may result in approximations.
Other iconic representations used to compare numerical values over
time or for different attributes are icons scaled based on value or par-
tially filled to encode percentages (Figure 5h). These representations
are engaging, as their animation mimics physical objects growing or
filling out. However, the effectiveness of these encodings is question-
able as our perception is not accurate for estimating areas [43]. We
opted to implement the filled icon, most commonly seen in data vid-
eos. The icon area filled with color encodes a value n out of a total
value m. Increasing (or decreasing) n causes the icon to appear to be
filling up (or empting out).
4.3 Clip Types
We briefly describe seven types of clip that we gathered empirically
from existing data videos, covering a wide range of animations and
attention cues (DC3). Note that a subset of these clip types correspond
to animations identified in the taxonomy presented in [31].
1. Creation and destruction: these clips provide animated sequences
to create a visualization (e.g., staggered appearance of bars) or to de-
stroy it (e.g., staggered disappearance of bars).
2. Cycling: these clips cycle through years to convey the evolution of
values as dynamic changes of the visualization (e.g., iterating over
percentages of females as filled icon year after year).
3. Accumulating: these clips gradually add data attributes to the visu-
alization. Most common ones are bar charts starting off with one series
(e.g. # of males) and then adding a second one, (e.g. # of females).
4. Transitions: these clips are rarely data-driven in existing videos.
Most transitions in these videos are a combination of destroy/create
clips rather than staged transitions, attempting to match elements of
both visualizations as described in [31]. We suspect that they are rarely
done due to the complexity of realizing them accurately in existing
video editing tools. We opted to support these clips in DataClips and
extended existing chart transitions [31] to and from pictographs.
5. Drill-down and roll-up: these clips support the transition to a subset
of the data visualized in a previous clip. For example, drill-down is
used when transitioning from a point in a line chart (for a specific year)
to an icon-filled representation of the percentage of female attendees
for this specific year.
6. Annotations: animations cover a variety of techniques that guide the
viewer’s attention to selected portions of the visualizations and reveal
specifics annotations. Commonly found examples include highlight-
ing or filtering elements of visualizations, adding a graphical or textual
annotation on a specific part of a visualization, or integrating reference
lines and numerical values to charts. Data videos also include annota-
tion clips in which icons are overlaid on top of standard data visuali-
zations. Icon annotations are used to distinguish between different
types of attributes (Figure 6a) or simply as an embellishment to make
the clip more engaging by personifying abstract data visualizations.
7. Multiple views: clips consisting of multiple views appeared less fre-
quently in the data video corpus we examined; these feature several
visualizations at once either side-by-side or as an overview+detail
setup. Due to their large screen real-estate and the small portions of
videos containing them, we decided not to include them.
4.4 Library Coverage
To demonstrate that the principles behind DataClips can lead to a wide
range of videos, we reproduced clips from a subset of the corpus of
over 70 data videos we analyzed. We demonstrate that Dataclips can
recreate about 87% of our corpus, albeit small differences in visual
design (e.g., icons placed beside growing bars instead of atop). We
describe our three levels analysis below:
Full coverage (31% of our corpus) refers to the ability to recreate
every data-driven clip included in a video with the current library of
implemented clips in DataClips.
Minor changes required (56%) refers to reproducibility with minor
changes to the implemented clips, or, the same animation is achievable
by replacing the original clip with another similar one selected from
the current library of implemented clips in DataClips.
Major changes required (13%) refers to the implementation of new
clips or new visualizations. An example of such a video is [5], includ-
ing custom data visualizations.
Figure 7 shows snapshots from two videos we recreated with
DataClips. Our companion website demonstrates how DataClips sup-
ports the authoring of these videos.
Fig. 5. Screenshot of the eight visualization type supported by DataClips. On the top row are
standard charts: (a) Line chart, (b) Bar, (c) Donut, and (d) Map; and on the bottom row are
pictograph-based representations: (e) Tally Pictograph, (f) Tally Pictograph-Comparison, (g)
Colored Pictographs, and (h) Filled Pictographs.
Fig. 6. Annotation clips: (a) male and female icon
embellishments above bars, (b) line chart with an-
notated value and reference lines, (c) bar chart
with highlight, and (d) US map with arrow annota-
tion for a given state.
4.5 Implementation
DataClips is a web application using a traditional client-server archi-
tecture with HTML, JavaScript, and CSS, as well as d3.js [22] for an-
imated visualizations. The client side follows a model–view–presenter
paradigm, using Backbone.js [4]. Each clip implements a model and a
view. This structure makes it easy to add a new data clip to the appli-
cation (DC4) (see website for code examples).
5 E
VALUATION
We conducted a user study to evaluate if non-experts could create data
videos using DataClips, and gain insights on how the authoring expe-
rience and output would compare to videos created with professional
tools. Our study was a between-subjects design with two sample
groups: one group of participants used DataClips; the other group used
Adobe Illustrator and After Effects, commonly used to create data vid-
eos. We asked participants to generate data-driven clips based on a list
of insights and accompanying dataset we provided. We report our
qualitative observations during this process and provide insights on
the quality of the videos generated from both groups by asking 40 dif-
ferent volunteers to rate them.
5.1 Participants
We recruited 12 participants (4 males, 8 females; aged 18–35) through
advertisements on university bulletin boards and email announce-
ments. The six participants who used DataClips had over 3 years of
experience in creating charts using Excel, but had no experience in
creating videos. The six participants in the Adobe Illustrator and After
Effects group had over two years of experience with this software, and
had created videos before. We rewarded participants with $50 at the
beginning of the session, independent of their performance.
5.2 Data and Experimental Material
We extracted data and insights from a data video on drug usage pub-
lished by The Guardian [11]. We selected this video because output
and data were publicly available, and it contained 20 facts of general
interest that did not require a specific sequence. The dataset contains
a set of statistics based on surveying 15,500 drug users via a global
drug survey. Instead of asking participants to create an entire video
following a narrative supported by a sequence of insights, we asked
them instead to create individual clips for a subset of facts they se-
lected, which could potentially be assembled later. We felt this ena-
bled to keep the study to a reasonable duration and avoided the skills
of participants in crafting a compelling narrative to interfere too much
with the quality of the final outcome. To ensure variety, we selected
the subset of ten insights most different from each other, out of 20
presented in the original video. Participants used the material in a
spreadsheet document, with each insight and corresponding data in
separate sheets. Table 2 shows an example insight and corresponding
dataset sample provided to the participants in the study.
Insight: In both US and UK, there are more Cannabis users than To-
bacco or Energy Drinks:
Dataset:
Country % Cannabis % Tobacco % Energy Drinks
UK 91 85 79
US 89 76 78
Table 2: Example of one insight and its corresponding data sample
The study was run in the lab, using a computer with a 1600x1900
screen resolution. In the Adobe group, participants were given the op-
tion to use their own laptop and other equipment like trackpad, stylus,
etc. All used Adobe Illustrator and After Effects CC 2015. Two of
them used their own laptop. In both groups, participants had access to
the internet in case they required to download images or icons. We
also provided them with sketching materials, such as blank sheets of
paper, color markers, and pens.
5.3 Procedure
We ran 2-hour long sessions. The experimenter asked participants to
think aloud and was present in the room to observe. The experimenter
started the session by showing four data videos with a high number of
views and including diverse types of data visualizations and anima-
tions. We divided the study in two main phases: (1) idea generation
and sketching, and (2) authoring.
In the idea generation phase, the experimenter first introduced da-
tasets and insights using a written sheet. Then participants were asked
to review each insight and rapidly sketch ideas for an animated visual
representation. The experimenter asked participants to generate story-
board-like sketches focusing on: (i) convey the insight to the general
public; (ii) select a visual representation that fits the data and insight
best; and (iii) think about possible types of animations, and additional
text or images required. Participants were encouraged to ask questions
if they needed clarification about the data or insights. At the end, the
experimenter asked them to describe the storyboards they had created.
In the authoring phase, the experimenter instructed participants to
select and implement as many of their sketched ideas as possible in
one hour. To motivate them to create high quality videos, we notified
them that all of the videos they produced would enter a contest and the
author with the highest ratings would win a prize. In the DataClips
group, the experimenter first demonstrated the capabilities of the tool
using an automated step-by-step tutorial included with the tool, using
Intro.js [9]. Participants could also ask questions about the system and
the instructor provided additional explanations. Note that this training
lasted from 15 to 20 minutes in addition to the one hour authoring
phase. The experimenter concluded the session with a semi-structured
interview asking participants about their overall authoring experience
and the issues they encountered, if any.
Fig. 7. Videos recreated about same sex marriage by The New York Times [15] (left) and statistics on remarriage by The Guardian [14] (right).
AMINI ET AL.: AUTHORING DATA-DRIVEN VIDEOS WITH DATACLIPS 507
4.2 Visualization Types
DataClips supports eight types of visualization: four standard charts,
and four pictographs representations (DC2), most commonly found in
data videos.
Standard charts (maps, bar, line, and donut charts). Figure 5a–d
shows standard charts in DataClips and the first column in Figure 5
shows the relative percentages found in our corpus. Note that, in line
with [8], more than half (59%) of the total data visualizations we ob-
served are standard charts.
Pictograph representations. Figure 5e–h present pictographs or iso-
types (International System of TYpographic Picture Education), en-
coding data using pictorial representations (icons). In the simplest
form, a pictograph or pictorial unit bar graph [23] divides the value to
encode into equal portions, each represented by one icon (Figure 5e).
Different categories can be distinguished by changing the icon shape
or color (Figure 5f). A variation of pictographs uses colored icons
(Figure 5g) to encode ratios and percentages. The proportion is repre-
sented by coloring n icons out of a total of m representing the total
value. Note that these representations may result in approximations.
Other iconic representations used to compare numerical values over
time or for different attributes are icons scaled based on value or par-
tially filled to encode percentages (Figure 5h). These representations
are engaging, as their animation mimics physical objects growing or
filling out. However, the effectiveness of these encodings is question-
able as our perception is not accurate for estimating areas [43]. We
opted to implement the filled icon, most commonly seen in data vid-
eos. The icon area filled with color encodes a value n out of a total
value m. Increasing (or decreasing) n causes the icon to appear to be
filling up (or empting out).
4.3 Clip Types
We briefly describe seven types of clip that we gathered empirically
from existing data videos, covering a wide range of animations and
attention cues (DC3). Note that a subset of these clip types correspond
to animations identified in the taxonomy presented in [31].
1. Creation and destruction: these clips provide animated sequences
to create a visualization (e.g., staggered appearance of bars) or to de-
stroy it (e.g., staggered disappearance of bars).
2. Cycling: these clips cycle through years to convey the evolution of
values as dynamic changes of the visualization (e.g., iterating over
percentages of females as filled icon year after year).
3. Accumulating: these clips gradually add data attributes to the visu-
alization. Most common ones are bar charts starting off with one series
(e.g. # of males) and then adding a second one, (e.g. # of females).
4. Transitions: these clips are rarely data-driven in existing videos.
Most transitions in these videos are a combination of destroy/create
clips rather than staged transitions, attempting to match elements of
both visualizations as described in [31]. We suspect that they are rarely
done due to the complexity of realizing them accurately in existing
video editing tools. We opted to support these clips in DataClips and
extended existing chart transitions [31] to and from pictographs.
5. Drill-down and roll-up: these clips support the transition to a subset
of the data visualized in a previous clip. For example, drill-down is
used when transitioning from a point in a line chart (for a specific year)
to an icon-filled representation of the percentage of female attendees
for this specific year.
6. Annotations: animations cover a variety of techniques that guide the
viewer’s attention to selected portions of the visualizations and reveal
specifics annotations. Commonly found examples include highlight-
ing or filtering elements of visualizations, adding a graphical or textual
annotation on a specific part of a visualization, or integrating reference
lines and numerical values to charts. Data videos also include annota-
tion clips in which icons are overlaid on top of standard data visuali-
zations. Icon annotations are used to distinguish between different
types of attributes (Figure 6a) or simply as an embellishment to make
the clip more engaging by personifying abstract data visualizations.
7. Multiple views: clips consisting of multiple views appeared less fre-
quently in the data video corpus we examined; these feature several
visualizations at once either side-by-side or as an overview+detail
setup. Due to their large screen real-estate and the small portions of
videos containing them, we decided not to include them.
4.4 Library Coverage
To demonstrate that the principles behind DataClips can lead to a wide
range of videos, we reproduced clips from a subset of the corpus of
over 70 data videos we analyzed. We demonstrate that Dataclips can
recreate about 87% of our corpus, albeit small differences in visual
design (e.g., icons placed beside growing bars instead of atop). We
describe our three levels analysis below:
Full coverage (31% of our corpus) refers to the ability to recreate
every data-driven clip included in a video with the current library of
implemented clips in DataClips.
Minor changes required (56%) refers to reproducibility with minor
changes to the implemented clips, or, the same animation is achievable
by replacing the original clip with another similar one selected from
the current library of implemented clips in DataClips.
Major changes required (13%) refers to the implementation of new
clips or new visualizations. An example of such a video is [5], includ-
ing custom data visualizations.
Figure 7 shows snapshots from two videos we recreated with
DataClips. Our companion website demonstrates how DataClips sup-
ports the authoring of these videos.
Fig. 5. Screenshot of the eight visualization type supported by DataClips. On the top row are
standard charts: (a) Line chart, (b) Bar, (c) Donut, and (d) Map; and on the bottom row are
pictograph-based representations: (e) Tally Pictograph, (f) Tally Pictograph-Comparison, (g)
Colored Pictographs, and (h) Filled Pictographs.
Fig. 6. Annotation clips: (a) male and female icon
embellishments above bars, (b) line chart with an-
notated value and reference lines, (c) bar chart
with highlight, and (d) US map with arrow annota-
tion for a given state.
4.5 Implementation
DataClips is a web application using a traditional client-server archi-
tecture with HTML, JavaScript, and CSS, as well as d3.js [22] for an-
imated visualizations. The client side follows a model–view–presenter
paradigm, using Backbone.js [4]. Each clip implements a model and a
view. This structure makes it easy to add a new data clip to the appli-
cation (DC4) (see website for code examples).
5 E
VALUATION
We conducted a user study to evaluate if non-experts could create data
videos using DataClips, and gain insights on how the authoring expe-
rience and output would compare to videos created with professional
tools. Our study was a between-subjects design with two sample
groups: one group of participants used DataClips; the other group used
Adobe Illustrator and After Effects, commonly used to create data vid-
eos. We asked participants to generate data-driven clips based on a list
of insights and accompanying dataset we provided. We report our
qualitative observations during this process and provide insights on
the quality of the videos generated from both groups by asking 40 dif-
ferent volunteers to rate them.
5.1 Participants
We recruited 12 participants (4 males, 8 females; aged 18–35) through
advertisements on university bulletin boards and email announce-
ments. The six participants who used DataClips had over 3 years of
experience in creating charts using Excel, but had no experience in
creating videos. The six participants in the Adobe Illustrator and After
Effects group had over two years of experience with this software, and
had created videos before. We rewarded participants with $50 at the
beginning of the session, independent of their performance.
5.2 Data and Experimental Material
We extracted data and insights from a data video on drug usage pub-
lished by The Guardian [11]. We selected this video because output
and data were publicly available, and it contained 20 facts of general
interest that did not require a specific sequence. The dataset contains
a set of statistics based on surveying 15,500 drug users via a global
drug survey. Instead of asking participants to create an entire video
following a narrative supported by a sequence of insights, we asked
them instead to create individual clips for a subset of facts they se-
lected, which could potentially be assembled later. We felt this ena-
bled to keep the study to a reasonable duration and avoided the skills
of participants in crafting a compelling narrative to interfere too much
with the quality of the final outcome. To ensure variety, we selected
the subset of ten insights most different from each other, out of 20
presented in the original video. Participants used the material in a
spreadsheet document, with each insight and corresponding data in
separate sheets. Table 2 shows an example insight and corresponding
dataset sample provided to the participants in the study.
Insight: In both US and UK, there are more Cannabis users than To-
bacco or Energy Drinks:
Dataset:
Country % Cannabis % Tobacco % Energy Drinks
UK 91 85 79
US 89 76 78
Table 2: Example of one insight and its corresponding data sample
The study was run in the lab, using a computer with a 1600x1900
screen resolution. In the Adobe group, participants were given the op-
tion to use their own laptop and other equipment like trackpad, stylus,
etc. All used Adobe Illustrator and After Effects CC 2015. Two of
them used their own laptop. In both groups, participants had access to
the internet in case they required to download images or icons. We
also provided them with sketching materials, such as blank sheets of
paper, color markers, and pens.
5.3 Procedure
We ran 2-hour long sessions. The experimenter asked participants to
think aloud and was present in the room to observe. The experimenter
started the session by showing four data videos with a high number of
views and including diverse types of data visualizations and anima-
tions. We divided the study in two main phases: (1) idea generation
and sketching, and (2) authoring.
In the idea generation phase, the experimenter first introduced da-
tasets and insights using a written sheet. Then participants were asked
to review each insight and rapidly sketch ideas for an animated visual
representation. The experimenter asked participants to generate story-
board-like sketches focusing on: (i) convey the insight to the general
public; (ii) select a visual representation that fits the data and insight
best; and (iii) think about possible types of animations, and additional
text or images required. Participants were encouraged to ask questions
if they needed clarification about the data or insights. At the end, the
experimenter asked them to describe the storyboards they had created.
In the authoring phase, the experimenter instructed participants to
select and implement as many of their sketched ideas as possible in
one hour. To motivate them to create high quality videos, we notified
them that all of the videos they produced would enter a contest and the
author with the highest ratings would win a prize. In the DataClips
group, the experimenter first demonstrated the capabilities of the tool
using an automated step-by-step tutorial included with the tool, using
Intro.js [9]. Participants could also ask questions about the system and
the instructor provided additional explanations. Note that this training
lasted from 15 to 20 minutes in addition to the one hour authoring
phase. The experimenter concluded the session with a semi-structured
interview asking participants about their overall authoring experience
and the issues they encountered, if any.
Fig. 7. Videos recreated about same sex marriage by The New York Times [15] (left) and statistics on remarriage by The Guardian [14] (right).
508 IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, VOL. 23, NO. 1, JANUARY 2017
5.4 Results
We recorded video and audio of sessions (which participants con-
sented to) and took notes during sessions. We analyzed these to ex-
tract: (1) selected insights and ideas with rationale, (2) pros and cons
vocalized by participants, and (3) the duration required for creating
each video clip. We also analyzed the artifacts produced by the partic-
ipants (storyboards and videos) and asked a group of 40 volunteers to
rate their quality. Study material and artifacts are available on our
companion website.
5.4.1 Generated Data Videos
We collected a total of 31 videos (each composed of one or more clips,
see Figure 8). Table 3 presents a set of interesting differences in the
quantity and nature of videos created in both groups.
To gather an independent opinion on their quality, we asked a sep-
arate group of 40 volunteers to view each video clip and rate them
from 1 (very poor) to 10 (excellent). We randomly presented each of
the videos generated, including clips extracted from the original video
from The Guardian (but without voice narration). We asked each of
the 40 volunteers to rate each video on a printed questionnaire. We
asked volunteers to simply provide their “overall impression” of each
video. These rankings would inform us of the following: (1) whether
videos generated by non-experts using DataClips were of sufficient
presentation quality to equal that of videos created by experts using
the Adobe suite; and (2) whether there were any trade-offs in using
DataClips, e.g., would participants create more videos with our tool,
but of lesser perceived quality. We summarize the outcome of these
rankings below, instead of presenting them in a separate discussion, to
provide a holistic view of the rankings in the context of the results
from authoring the clips.
More clips with more variations with DataClips. Participants using
DataClips were non-experts in video editing but still generated more
clips than did experts using Adobe software. As we expected, the av-
erage time to create the first clip was considerably shorter with
DataClips. However, we were surprised to observe that clips created
with Adobe did not employ custom data visualizations (beyond stand-
ard charts) given the freedom offered to the users. Overall, we counted
seven types of visualization covered in DataClips whereas only three
different ones were used in videos created with the Adobe software.
Data-driven clips. Perhaps one objective measure of the quality of a
data-driven video is its accuracy regarding the data it conveys. As data
binding comes for free with DataClips, all visualizations occurring in
the videos were accurate without additional effort from the user. How-
ever, with Adobe software, participants did not always create accurate
visual encodings. When asked, participant 2 (PA2) replied by saying
that “it does not matter if the bar height is not exact.” Pointing to the
resulting video, PA2 also commented that “this is not supposed to be
read by machines but by humans who don’t care about the exact num-
bers.” While this issue may not prove crucial for professional design-
ers or data analysts with a strong background in visual perception, it
is certainly concerning for non-experts.
Similar rating between sources. We find that volunteers’ overall im-
pression of videos created with DataClips was equivalent to that of
videos generated using the Adobe tools, and even more interestingly,
equivalent to video clips extracted from the initial video produced by
The Guardian (note however that we removed the voice narration to
compare to our other two conditions). Figure 9 illustrates the ratio of
average rankings between the three conditions. Through calculating
the weighted rank average, we find 36% of the rankings were in favor
of DataClips, 35% on average in favor of the Adobe tools, and 29%
for the data videos from The Guardian. This outcome indicates that
the perceived quality of videos with DataClips matched that of the
other two conditions, despite the former being created by non-experts.
Overall, these findings are encouraging, as they indicate that par-
ticipants who were non-expert in video editing generated more videos
with DataClips than experienced participants did with professional
Adobe software, without any apparent differences in viewers’ rating.
Fig. 9. Ratio of the average viewer rankings for videos produced with
DataClips, Adobe Illustrator/After Effects, and clips from the initial video
from The Guardian.
Authoring
Tool(s)
Clip Sequences
Average # of
Clip Sequences
Average # of
Clips
Distinct Insights
(out of 10)
Distinct Vis
Types
Average Time
(first video)
Average Time
(all videos)
DataClips 23 3.83 2.5 8 7 9.4 15.8
Adobe
Software
8 1.33 1.2 5 3 45.1 37.5
Table. 3. Numbers for distinct items used as well as the average time
(
in minutes
)
it took
p
artici
p
ants to create each video.
Fig. 8. Examples of clips created by our participants in the DataClips group (top), Adobe software group (middle), and The Guardian example we
found online (bottom) based on the same dataset [11].
5.4.2 Authoring Experience
Our analysis of recordings led to three major insights regarding the
use of DataClips to create data-driven videos.
Learnability. We confirmed that all six non-expert participants using
DataClips were able to learn and use the main features of DataClips
with a short training period. The data configuration was perhaps the
most difficult for them to understand. However, we observed that par-
ticipants could correct their mistakes without intervention from the ex-
perimenter and find configuration options for each clip without much
difficulty. We also hypothesize that a part of the difficulties partici-
pants encountered was due to the lack of familiarity with the dataset,
as we observed a lot of back and forth between spreadsheet and list of
column names in the DataClips panel.
Rapid ideation. We observed that 11 clips (out of 23) created by par-
ticipants with DataClips had some changes compared to their initial
storyboard. While four of these were relatively minor changes, due to
lack of control provided by DataClips regarding the layout or type of
animation effects, other changes were more substantial. We observed
at least three particular instances in which participants changed their
design upon investigation of the clip library. Their comments indi-
cated that they saw a more visually interesting clip or a clip better
suited for their data. For example, regarding the use of a standard pie
chart, PD2 stated that “I guess I can show the percentage better using
this [the filled icon clip].” We believe this ability for non-experts to
ideate on how to convey data insights is important.
Rapid prototyping. The average number of videos generated by the
group with Adobe software was less than 2. All of the videos also only
featured standard data visualizations and animations. All participants
reported on the lack of time for them to achieve what they wanted.
PA1, PA4, and PA5 commented that “if I had more time, I could have
[…]” PA1 remarked that “if I had more time, I could have made the
video more sophisticated.” From their comments, we also noted that
most of these participants avoided picking certain insights (e.g., trend
data) due to the amount of work it would require them to produce the
video. We did not observe any of these issues with the participants
using DataClips. These results suggest that even a more experienced
video editing audience could benefit from using DataClips as a rapid
prototyping tool before polishing videos with professional software
suites like Adobe Illustrator/After Effects.
6 D
ISCUSSION AND LIMITATIONS
Results of our study show that our six non-expert video editing partic-
ipants could generate more videos with DataClips than expert video
editing participants with professional software. In addition, ratings
tend to indicate that the perceived quality of videos created with both
tools is equivalent. Additional insights on the authoring process appear
to confirm that DataClips can lower the barrier to creating data-driven
videos and possibly fulfill both of our intended usage scenarios.
However, as with all qualitative studies with small sample size,
these results should be treated with caution and DataClips warrants
further evaluation to confirm if our initial insights apply more gener-
ally. In particular, we did not compare DataClips to other software de-
signed for enabling non-experts to create data videos. Note that during
a pilot, we had initially included a group in which non-expert partici-
pants used Microsoft PowerPoint to create data videos, reasoning that
this software might be used by non-experts to create animated data
visualizations. However, we decided to discard the group, as our two
pilot participants struggled to create even a single data-driven clip.
Furthermore, we opted to use Adobe software due to the lack of an
existing tool supporting the same capabilities as in DataClips. The pur-
pose of Adobe software is much broader than authoring data videos
and proper utilization of all its features requires a larger time window.
Another limitation of our study is the evaluation of the quality of
the videos generated. Assessing data-driven storytelling media (and
data-driven videos) remains an open research question in our commu-
nity. Our study provides a first step into assessing data video quality
but does not delve into all relevant metrics (e.g., engagement, memo-
rability). Thus, we cannot make any assertions regarding the effective-
ness of communication and evaluating the quality of data videos re-
mains an open question.
Our observations also pointed to the need for several iterations of
the tool. For example, participants asked for more control over the lay-
out, which could easily be enabled via direct manipulation. Similarly,
it is also possible to add other features like “the ability to include a
voice narrations” to add richness to the videos produced. A less
straightforward iteration relates to control over the animation effects
and the addition of variations of clips. There is a tradeoff between add-
ing several variations of a clip to the library versus providing a generic
clip with more configuration settings. In the first case, searching
through a large library might become cumbersome and overwhelm
first-time users. In the second case, the task of configuring a high num-
ber of parameters may also become cumbersome, and labeling them
meaningfully for use by non-experts is not trivial. We aim at iterating
over the design to strike the right balance.
7 C
ONCLUSION AND FUTURE WORK
As interest in presenting data moves beyond the confines of data ana-
lysts, more general-purpose tools are needed to allow the easy creation
of data-driven stories. In this paper, we introduce DataClips, an au-
thoring tool for creating data videos, aimed at non-experts. We devel-
oped DataClips based on our close examination of 70 data videos
available in the mainstream media and developed by reputed data jour-
nalism organizations. From this exploration, we identified the major
components necessary for creating compelling data videos. This in-
cludes a significant library of data clips, which allow presenting data-
driven insights using different visualization styles (including picto-
graphs) as well as different methods for engaging viewers through the
use of motion graphics. We report on our design rationale for
DataClips, its implementation details, and a qualitative evaluation.
From the latter, we find that non-experts can create data videos having
the same visual caliber as those created using commercial animation
tools (not necessarily for data videos) but also can create more videos
in a limited time than tools currently used for creating such videos.
In future work, we envision two main research directions. First, we
aim at enabling non-experts to craft compelling narratives [19] for
data-driven videos by offering a set of templates geared toward differ-
ent styles of insights based on the intended message. Second, we will
deepen our understanding of what makes compelling data videos. We
aim at exploring different evaluation methods and metrics to attempt
to better capture the characteristics that make good data videos.
A
CKNOWLEDGMENTS
The authors wish to thank the usage scenario professionals for their
feedback and the user study participants for their time. This research
was partially funded by Microsoft Research and an NSERC Strategic
Grant awarded to Pourang Irani.
8 R
EFERENCES
[1] Adobe illustrator cc. http://www.adobe.com/ca/products/illustra-
tor.html/, AdobeIllustrator. [Online; accessed 31-March-2016].
[2] Adobeaftereffects cc. http://www.adobe.com/ca/products/afteref-
fects.html/, Adobe AfterEffects. [Online; accessed 31-March-2016].
[3] Animoto. http://animoto.com/.
[4] Backbone.js. http://backbonejs.org/, Backbonejs. [Online; accessed 31-
March-2016].
[5] The fallen of world war ii. http://www.fallen.io/ww2/, Fallen. [Online;
accessed 31-March-2016].
[6] FLOWINGDATA. http://flowingdata.com/.
[7] The Guardian Datablog. http://www.theguardian.com/data/.
[8] iMovie. https://www.apple.com/mac/imovie/.
[9] Intro.js. http://introjs.com/, Introjs. [Online; accessed 31-March-2016].
[10] The New York Times. http://www.nytimes.com/.
AMINI ET AL.: AUTHORING DATA-DRIVEN VIDEOS WITH DATACLIPS 509
5.4 Results
We recorded video and audio of sessions (which participants con-
sented to) and took notes during sessions. We analyzed these to ex-
tract: (1) selected insights and ideas with rationale, (2) pros and cons
vocalized by participants, and (3) the duration required for creating
each video clip. We also analyzed the artifacts produced by the partic-
ipants (storyboards and videos) and asked a group of 40 volunteers to
rate their quality. Study material and artifacts are available on our
companion website.
5.4.1 Generated Data Videos
We collected a total of 31 videos (each composed of one or more clips,
see Figure 8). Table 3 presents a set of interesting differences in the
quantity and nature of videos created in both groups.
To gather an independent opinion on their quality, we asked a sep-
arate group of 40 volunteers to view each video clip and rate them
from 1 (very poor) to 10 (excellent). We randomly presented each of
the videos generated, including clips extracted from the original video
from The Guardian (but without voice narration). We asked each of
the 40 volunteers to rate each video on a printed questionnaire. We
asked volunteers to simply provide their “overall impression” of each
video. These rankings would inform us of the following: (1) whether
videos generated by non-experts using DataClips were of sufficient
presentation quality to equal that of videos created by experts using
the Adobe suite; and (2) whether there were any trade-offs in using
DataClips, e.g., would participants create more videos with our tool,
but of lesser perceived quality. We summarize the outcome of these
rankings below, instead of presenting them in a separate discussion, to
provide a holistic view of the rankings in the context of the results
from authoring the clips.
More clips with more variations with DataClips. Participants using
DataClips were non-experts in video editing but still generated more
clips than did experts using Adobe software. As we expected, the av-
erage time to create the first clip was considerably shorter with
DataClips. However, we were surprised to observe that clips created
with Adobe did not employ custom data visualizations (beyond stand-
ard charts) given the freedom offered to the users. Overall, we counted
seven types of visualization covered in DataClips whereas only three
different ones were used in videos created with the Adobe software.
Data-driven clips. Perhaps one objective measure of the quality of a
data-driven video is its accuracy regarding the data it conveys. As data
binding comes for free with DataClips, all visualizations occurring in
the videos were accurate without additional effort from the user. How-
ever, with Adobe software, participants did not always create accurate
visual encodings. When asked, participant 2 (PA2) replied by saying
that “it does not matter if the bar height is not exact.” Pointing to the
resulting video, PA2 also commented that “this is not supposed to be
read by machines but by humans who don’t care about the exact num-
bers.” While this issue may not prove crucial for professional design-
ers or data analysts with a strong background in visual perception, it
is certainly concerning for non-experts.
Similar rating between sources. We find that volunteers’ overall im-
pression of videos created with DataClips was equivalent to that of
videos generated using the Adobe tools, and even more interestingly,
equivalent to video clips extracted from the initial video produced by
The Guardian (note however that we removed the voice narration to
compare to our other two conditions). Figure 9 illustrates the ratio of
average rankings between the three conditions. Through calculating
the weighted rank average, we find 36% of the rankings were in favor
of DataClips, 35% on average in favor of the Adobe tools, and 29%
for the data videos from The Guardian. This outcome indicates that
the perceived quality of videos with DataClips matched that of the
other two conditions, despite the former being created by non-experts.
Overall, these findings are encouraging, as they indicate that par-
ticipants who were non-expert in video editing generated more videos
with DataClips than experienced participants did with professional
Adobe software, without any apparent differences in viewers’ rating.
Fig. 9. Ratio of the average viewer rankings for videos produced with
DataClips, Adobe Illustrator/After Effects, and clips from the initial video
from The Guardian.
Authoring
Tool(s)
Clip Sequences
Average # of
Clip Sequences
Average # of
Clips
Distinct Insights
(out of 10)
Distinct Vis
Types
Average Time
(first video)
Average Time
(all videos)
DataClips 23 3.83 2.5 8 7 9.4 15.8
Adobe
Software
8 1.33 1.2 5 3 45.1 37.5
Table. 3. Numbers for distinct items used as well as the average time
(
in minutes
)
it took
p
artici
p
ants to create each video.
Fig. 8. Examples of clips created by our participants in the DataClips group (top), Adobe software group (middle), and The Guardian example we
found online (bottom) based on the same dataset [11].
5.4.2 Authoring Experience
Our analysis of recordings led to three major insights regarding the
use of DataClips to create data-driven videos.
Learnability. We confirmed that all six non-expert participants using
DataClips were able to learn and use the main features of DataClips
with a short training period. The data configuration was perhaps the
most difficult for them to understand. However, we observed that par-
ticipants could correct their mistakes without intervention from the ex-
perimenter and find configuration options for each clip without much
difficulty. We also hypothesize that a part of the difficulties partici-
pants encountered was due to the lack of familiarity with the dataset,
as we observed a lot of back and forth between spreadsheet and list of
column names in the DataClips panel.
Rapid ideation. We observed that 11 clips (out of 23) created by par-
ticipants with DataClips had some changes compared to their initial
storyboard. While four of these were relatively minor changes, due to
lack of control provided by DataClips regarding the layout or type of
animation effects, other changes were more substantial. We observed
at least three particular instances in which participants changed their
design upon investigation of the clip library. Their comments indi-
cated that they saw a more visually interesting clip or a clip better
suited for their data. For example, regarding the use of a standard pie
chart, PD2 stated that “I guess I can show the percentage better using
this [the filled icon clip].” We believe this ability for non-experts to
ideate on how to convey data insights is important.
Rapid prototyping. The average number of videos generated by the
group with Adobe software was less than 2. All of the videos also only
featured standard data visualizations and animations. All participants
reported on the lack of time for them to achieve what they wanted.
PA1, PA4, and PA5 commented that “if I had more time, I could have
[…]” PA1 remarked that “if I had more time, I could have made the
video more sophisticated.” From their comments, we also noted that
most of these participants avoided picking certain insights (e.g., trend
data) due to the amount of work it would require them to produce the
video. We did not observe any of these issues with the participants
using DataClips. These results suggest that even a more experienced
video editing audience could benefit from using DataClips as a rapid
prototyping tool before polishing videos with professional software
suites like Adobe Illustrator/After Effects.
6 D
ISCUSSION AND LIMITATIONS
Results of our study show that our six non-expert video editing partic-
ipants could generate more videos with DataClips than expert video
editing participants with professional software. In addition, ratings
tend to indicate that the perceived quality of videos created with both
tools is equivalent. Additional insights on the authoring process appear
to confirm that DataClips can lower the barrier to creating data-driven
videos and possibly fulfill both of our intended usage scenarios.
However, as with all qualitative studies with small sample size,
these results should be treated with caution and DataClips warrants
further evaluation to confirm if our initial insights apply more gener-
ally. In particular, we did not compare DataClips to other software de-
signed for enabling non-experts to create data videos. Note that during
a pilot, we had initially included a group in which non-expert partici-
pants used Microsoft PowerPoint to create data videos, reasoning that
this software might be used by non-experts to create animated data
visualizations. However, we decided to discard the group, as our two
pilot participants struggled to create even a single data-driven clip.
Furthermore, we opted to use Adobe software due to the lack of an
existing tool supporting the same capabilities as in DataClips. The pur-
pose of Adobe software is much broader than authoring data videos
and proper utilization of all its features requires a larger time window.
Another limitation of our study is the evaluation of the quality of
the videos generated. Assessing data-driven storytelling media (and
data-driven videos) remains an open research question in our commu-
nity. Our study provides a first step into assessing data video quality
but does not delve into all relevant metrics (e.g., engagement, memo-
rability). Thus, we cannot make any assertions regarding the effective-
ness of communication and evaluating the quality of data videos re-
mains an open question.
Our observations also pointed to the need for several iterations of
the tool. For example, participants asked for more control over the lay-
out, which could easily be enabled via direct manipulation. Similarly,
it is also possible to add other features like “the ability to include a
voice narrations” to add richness to the videos produced. A less
straightforward iteration relates to control over the animation effects
and the addition of variations of clips. There is a tradeoff between add-
ing several variations of a clip to the library versus providing a generic
clip with more configuration settings. In the first case, searching
through a large library might become cumbersome and overwhelm
first-time users. In the second case, the task of configuring a high num-
ber of parameters may also become cumbersome, and labeling them
meaningfully for use by non-experts is not trivial. We aim at iterating
over the design to strike the right balance.
7 C
ONCLUSION AND FUTURE WORK
As interest in presenting data moves beyond the confines of data ana-
lysts, more general-purpose tools are needed to allow the easy creation
of data-driven stories. In this paper, we introduce DataClips, an au-
thoring tool for creating data videos, aimed at non-experts. We devel-
oped DataClips based on our close examination of 70 data videos
available in the mainstream media and developed by reputed data jour-
nalism organizations. From this exploration, we identified the major
components necessary for creating compelling data videos. This in-
cludes a significant library of data clips, which allow presenting data-
driven insights using different visualization styles (including picto-
graphs) as well as different methods for engaging viewers through the
use of motion graphics. We report on our design rationale for
DataClips, its implementation details, and a qualitative evaluation.
From the latter, we find that non-experts can create data videos having
the same visual caliber as those created using commercial animation
tools (not necessarily for data videos) but also can create more videos
in a limited time than tools currently used for creating such videos.
In future work, we envision two main research directions. First, we
aim at enabling non-experts to craft compelling narratives [19] for
data-driven videos by offering a set of templates geared toward differ-
ent styles of insights based on the intended message. Second, we will
deepen our understanding of what makes compelling data videos. We
aim at exploring different evaluation methods and metrics to attempt
to better capture the characteristics that make good data videos.
A
CKNOWLEDGMENTS
The authors wish to thank the usage scenario professionals for their
feedback and the user study participants for their time. This research
was partially funded by Microsoft Research and an NSERC Strategic
Grant awarded to Pourang Irani.
8 R
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